semantic-segmentation-of-plants-with-detectron-2.log

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,{"stream_name":"stdout","time":30.207797875,"data":"Requirement already satisfied: pyasn1\u003c0.5.0,\u003e=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules\u003e=0.2.1-\u003egoogle-auth\u003c3,\u003e=1.6.3-\u003etensorboard-\u003edetectron2==0.6) (0.4.8)\r\n"}
,{"stream_name":"stdout","time":30.20781625,"data":"Requirement already satisfied: oauthlib\u003e=3.0.0 in /opt/conda/lib/python3.10/site-packages (from requests-oauthlib\u003e=0.7.0-\u003egoogle-auth-oauthlib\u003c1.1,\u003e=0.5-\u003etensorboard-\u003edetectron2==0.6) (3.2.2)\r\n"}
,{"stream_name":"stdout","time":30.310920006,"data":"Building wheels for collected packages: detectron2, fvcore, antlr4-python3-runtime\r\n"}
,{"stream_name":"stdout","time":201.216497586,"data":"  Building wheel for detectron2 (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\r\n"}
,{"stream_name":"stdout","time":201.216546505,"data":"\u001b[?25h  Created wheel for detectron2: filename=detectron2-0.6-cp310-cp310-linux_x86_64.whl size=1255108 sha256=c1bb4c192a8a85f1c87b6eff3d9a1bcc8cb87afba91bb90713b6d52f7d2df0de\r\n"}
,{"stream_name":"stdout","time":201.216554265,"data":"  Stored in directory: /tmp/pip-ephem-wheel-cache-94a0tk58/wheels/47/e5/15/94c80df2ba85500c5d76599cc307c0a7079d0e221bb6fc4375\r\n"}
,{"stream_name":"stdout","time":202.484371077,"data":"  Building wheel for fvcore (setup.py) ... \u001b[?25l-\b \bdone\r\n"}
,{"stream_name":"stdout","time":202.484412358,"data":"\u001b[?25h  Created wheel for fvcore: filename=fvcore-0.1.5.post20221221-py3-none-any.whl size=61405 sha256=bbcfeea57f018330174c976c074113dedf0a54d49dc1e2e15cc21ebd5d4805e8\r\n"}
,{"stream_name":"stdout","time":202.484420144,"data":"  Stored in directory: /root/.cache/pip/wheels/01/c0/af/77c1cf53a1be9e42a52b48e5af2169d40ec2e89f7362489dd0\r\n"}
,{"stream_name":"stdout","time":203.802844341,"data":"  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l-\b \bdone\r\n"}
,{"stream_name":"stdout","time":203.802882933,"data":"\u001b[?25h  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=9dc6457d743afbce3116c7471223a132c614f7213c1f42c7b1d3e2e6ebbab799\r\n"}
,{"stream_name":"stdout","time":203.802890735,"data":"  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\r\n"}
,{"stream_name":"stdout","time":203.802910449,"data":"Successfully built detectron2 fvcore antlr4-python3-runtime\r\n"}
,{"stream_name":"stdout","time":215.46712805,"data":"Installing collected packages: antlr4-python3-runtime, yacs, portalocker, pathspec, packaging, omegaconf, iopath, hydra-core, black, pycocotools, fvcore, detectron2\r\n"}
,{"stream_name":"stdout","time":215.669267295,"data":"  Attempting uninstall: packaging\r\n"}
,{"stream_name":"stdout","time":215.669311267,"data":"    Found existing installation: packaging 21.3\r\n"}
,{"stream_name":"stdout","time":215.66931776,"data":"    Uninstalling packaging-21.3:\r\n"}
,{"stream_name":"stdout","time":215.72093058,"data":"      Successfully uninstalled packaging-21.3\r\n"}
,{"stream_name":"stdout","time":217.038332118,"data":"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n"}
,{"stream_name":"stdout","time":217.038369214,"data":"cudf 23.8.0 requires cupy-cuda11x\u003e=12.0.0, which is not installed.\r\n"}
,{"stream_name":"stdout","time":217.038380177,"data":"cuml 23.8.0 requires cupy-cuda11x\u003e=12.0.0, which is not installed.\r\n"}
,{"stream_name":"stdout","time":217.038387024,"data":"dask-cudf 23.8.0 requires cupy-cuda11x\u003e=12.0.0, which is not installed.\r\n"}
,{"stream_name":"stdout","time":217.038392055,"data":"cudf 23.8.0 requires pandas\u003c1.6.0dev0,\u003e=1.3, but you have pandas 2.0.2 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038397376,"data":"cudf 23.8.0 requires protobuf\u003c5,\u003e=4.21, but you have protobuf 3.20.3 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038402616,"data":"cuml 23.8.0 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.03840767,"data":"dask-cuda 23.8.0 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038412866,"data":"dask-cuda 23.8.0 requires pandas\u003c1.6.0dev0,\u003e=1.3, but you have pandas 2.0.2 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038417787,"data":"dask-cudf 23.8.0 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.03842299,"data":"dask-cudf 23.8.0 requires pandas\u003c1.6.0dev0,\u003e=1.3, but you have pandas 2.0.2 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038427905,"data":"distributed 2023.7.1 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038433206,"data":"google-cloud-bigquery 2.34.4 requires packaging\u003c22.0dev,\u003e=14.3, but you have packaging 23.1 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038438437,"data":"jupyterlab-lsp 4.2.0 requires jupyter-lsp\u003e=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038443813,"data":"momepy 0.6.0 requires shapely\u003e=2, but you have shapely 1.8.5.post1 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038449692,"data":"pymc3 3.11.5 requires numpy\u003c1.22.2,\u003e=1.15.0, but you have numpy 1.23.5 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038459679,"data":"pymc3 3.11.5 requires scipy\u003c1.8.0,\u003e=1.7.3, but you have scipy 1.11.2 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038469082,"data":"raft-dask 23.8.0 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"}
,{"stream_name":"stdout","time":217.038487331,"data":"ydata-profiling 4.3.1 requires scipy\u003c1.11,\u003e=1.4.1, but you have scipy 1.11.2 which is incompatible.\u001b[0m\u001b[31m\r\n"}
,{"stream_name":"stdout","time":217.038494464,"data":"\u001b[0mSuccessfully installed antlr4-python3-runtime-4.9.3 black-23.9.1 detectron2-0.6 fvcore-0.1.5.post20221221 hydra-core-1.3.2 iopath-0.1.9 omegaconf-2.3.0 packaging-23.1 pathspec-0.11.2 portalocker-2.8.2 pycocotools-2.0.7 yacs-0.1.8\r\n"}
,{"stream_name":"stdout","time":219.774063631,"data":"2.0.0 True\n"}
,{"stream_name":"stdout","time":220.700549514,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0m\n"}
,{"stream_name":"stdout","time":220.700602938,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"}
,{"stream_name":"stdout","time":220.700611439,"data":"\n"}
,{"stream_name":"stdout","time":220.702501643,"data":"\u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0mLoaded 123 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/train/train.json\n"}
,{"stream_name":"stdout","time":220.744890243,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0m\n"}
,{"stream_name":"stdout","time":220.744911035,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"}
,{"stream_name":"stdout","time":220.744927809,"data":"\n"}
,{"stream_name":"stdout","time":220.746546326,"data":"\u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0mLoaded 123 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/train/train.json\n"}
,{"stream_name":"stdout","time":220.788803161,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0m\n"}
,{"stream_name":"stdout","time":220.788823346,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"}
,{"stream_name":"stdout","time":220.788830261,"data":"\n"}
,{"stream_name":"stdout","time":220.789918143,"data":"\u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0mLoaded 18 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/valid/valid.json\n"}
,{"stream_name":"stdout","time":227.179968614,"data":"\u001b[32m[09/23 07:38:42 d2.engine.defaults]: \u001b[0mModel:\n"}
,{"stream_name":"stdout","time":227.180007033,"data":"GeneralizedRCNN(\n"}
,{"stream_name":"stdout","time":227.180015259,"data":"  (backbone): FPN(\n"}
,{"stream_name":"stdout","time":227.180020811,"data":"    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n"}
,{"stream_name":"stdout","time":227.180026353,"data":"    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"}
,{"stream_name":"stdout","time":227.180031327,"data":"    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n"}
,{"stream_name":"stdout","time":227.180037021,"data":"    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"}
,{"stream_name":"stdout","time":227.180041947,"data":"    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n"}
,{"stream_name":"stdout","time":227.180046859,"data":"    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"}
,{"stream_name":"stdout","time":227.180051985,"data":"    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n"}
,{"stream_name":"stdout","time":227.180056954,"data":"    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"}
,{"stream_name":"stdout","time":227.180072911,"data":"    (top_block): LastLevelMaxPool()\n"}
,{"stream_name":"stdout","time":227.180077739,"data":"    (bottom_up): ResNet(\n"}
,{"stream_name":"stdout","time":227.180084569,"data":"      (stem): BasicStem(\n"}
,{"stream_name":"stdout","time":227.180105523,"data":"        (conv1): Conv2d(\n"}
,{"stream_name":"stdout","time":227.180113641,"data":"          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n"}
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,{"stream_name":"stdout","time":227.180123293,"data":"        )\n"}
,{"stream_name":"stdout","time":227.180128394,"data":"      )\n"}
,{"stream_name":"stdout","time":227.180132861,"data":"      (res2): Sequential(\n"}
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,{"stream_name":"stdout","time":227.180142884,"data":"          (shortcut): Conv2d(\n"}
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,{"stream_name":"stdout","time":227.180152143,"data":"            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"}
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,{"stream_name":"stdout","time":227.18016138,"data":"          (conv1): Conv2d(\n"}
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,{"stream_name":"stdout","time":227.196109555,"data":"\u001b[32m[09/23 07:38:42 d2.data.datasets.coco]: \u001b[0mLoaded 123 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/train/train.json\n"}
,{"stream_name":"stdout","time":227.202177857,"data":"\u001b[32m[09/23 07:38:42 d2.data.build]: \u001b[0mRemoved 0 images with no usable annotations. 123 images left.\n"}
,{"stream_name":"stdout","time":227.21499769,"data":"\u001b[32m[09/23 07:38:42 d2.data.build]: \u001b[0mDistribution of instances among all 4 categories:\n"}
,{"stream_name":"stdout","time":227.215031132,"data":"\u001b[36m|   category    | #instances   | category   | #instances   | category   | #instances   |\n"}
,{"stream_name":"stdout","time":227.2150391,"data":"|:-------------:|:-------------|:-----------|:-------------|:-----------|:-------------|\n"}
,{"stream_name":"stdout","time":227.215057176,"data":"| leaf, flowe.. | 0            | 0          | 440          | 1          | 164          |\n"}
,{"stream_name":"stdout","time":227.215063542,"data":"|       2       | 114          |            |              |            |              |\n"}
,{"stream_name":"stdout","time":227.215069607,"data":"|     total     | 718          |            |              |            |              |\u001b[0m\n"}
,{"stream_name":"stdout","time":227.21704684,"data":"\u001b[32m[09/23 07:38:42 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]\n"}
,{"stream_name":"stdout","time":227.218765315,"data":"\u001b[32m[09/23 07:38:42 d2.data.build]: \u001b[0mUsing training sampler TrainingSampler\n"}
,{"stream_name":"stdout","time":227.220234089,"data":"\u001b[32m[09/23 07:38:42 d2.data.common]: \u001b[0mSerializing the dataset using: \u003cclass 'detectron2.data.common._TorchSerializedList'\u003e\n"}
,{"stream_name":"stdout","time":227.224137052,"data":"\u001b[32m[09/23 07:38:42 d2.data.common]: \u001b[0mSerializing 123 elements to byte tensors and concatenating them all ...\n"}
,{"stream_name":"stdout","time":227.230644552,"data":"\u001b[32m[09/23 07:38:42 d2.data.common]: \u001b[0mSerialized dataset takes 0.17 MiB\n"}
,{"stream_name":"stdout","time":227.232131018,"data":"\u001b[32m[09/23 07:38:42 d2.checkpoint.detection_checkpoint]: \u001b[0m[DetectionCheckpointer] Loading from https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl ...\n"}
,{"stream_name":"stderr","time":228.472521436,"data":"\rmodel_final_f10217.pkl: 0.00B [00:00, ?B/s]\rmodel_final_f10217.pkl:   0%|          | 8.19k/178M [00:00\u003c44:21, 66.8kB/s]\rmodel_final_f10217.pkl:   3%|▎         | 4.88M/178M [00:00\u003c00:06, 25.2MB/s]\rmodel_final_f10217.pkl:   7%|▋         | 12.7M/178M [00:00\u003c00:03, 47.7MB/s]\rmodel_final_f10217.pkl:  13%|█▎        | 22.4M/178M [00:00\u003c00:02, 66.1MB/s]\rmodel_final_f10217.pkl:  20%|█▉        | 34.8M/178M [00:00\u003c00:01, 86.4MB/s]\rmodel_final_f10217.pkl:  28%|██▊       | 49.2M/178M [00:00\u003c00:01, 106MB/s] \rmodel_final_f10217.pkl:  38%|███▊      | 67.8M/178M [00:00\u003c00:00, 132MB/s]\rmodel_final_f10217.pkl:  51%|█████     | 90.0M/178M [00:00\u003c00:00, 160MB/s]\rmodel_final_f10217.pkl:  64%|██████▎   | 113M/178M [00:00\u003c00:00, 183MB/s] \rmodel_final_f10217.pkl:  77%|███████▋  | 136M/178M [00:01\u003c00:00, 198MB/s]\rmodel_final_f10217.pkl:  90%|████████▉ | 160M/178M [00:01\u003c00:00, 208MB/s]\rmodel_final_f10217.pkl: 178MB [00:01, 147MB/s]                           \n"}
,{"stream_name":"stdout","time":228.653681696,"data":"\u001b[32m[09/23 07:38:44 d2.engine.train_loop]: \u001b[0mStarting training from iteration 0\n"}
,{"stream_name":"stderr","time":241.278435168,"data":"/opt/conda/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /usr/local/src/pytorch/aten/src/ATen/native/TensorShape.cpp:3483.)\n"}
,{"stream_name":"stderr","time":241.27850688,"data":"  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"}
,{"stream_name":"stdout","time":251.518974705,"data":"\u001b[32m[09/23 07:39:07 d2.utils.events]: \u001b[0m eta: 0:05:39  iter: 19  total_loss: 2.828  loss_cls: 1.447  loss_box_reg: 0.4917  loss_mask: 0.6928  loss_rpn_cls: 0.1044  loss_rpn_loc: 0.02792    time: 0.3470  last_time: 0.3698  data_time: 0.0158  last_data_time: 0.0070   lr: 4.9953e-06  max_mem: 1767M\n"}
,{"stream_name":"stderr","time":260.105853923,"data":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version \u003e=1.16.5 and \u003c1.23.0 is required for this version of SciPy (detected version 1.23.5\n"}
,{"stream_name":"stderr","time":260.105889154,"data":"  warnings.warn(f\"A NumPy version \u003e={np_minversion} and \u003c{np_maxversion}\"\n"}
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,{"stream_name":"stdout","time":517.562705788,"data":"\u001b[32m[09/23 07:43:33 d2.utils.events]: \u001b[0m eta: 0:01:49  iter: 699  total_loss: 0.6694  loss_cls: 0.1057  loss_box_reg: 0.2457  loss_mask: 0.2652  loss_rpn_cls: 0.003795  loss_rpn_loc: 0.01363    time: 0.3703  last_time: 0.3294  data_time: 0.0076  last_data_time: 0.0089   lr: 0.00017483  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":524.91804221,"data":"\u001b[32m[09/23 07:43:40 d2.utils.events]: \u001b[0m eta: 0:01:42  iter: 719  total_loss: 0.8476  loss_cls: 0.1678  loss_box_reg: 0.311  loss_mask: 0.3076  loss_rpn_cls: 0.008928  loss_rpn_loc: 0.02535    time: 0.3702  last_time: 0.4312  data_time: 0.0072  last_data_time: 0.0072   lr: 0.00017982  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":532.605087723,"data":"\u001b[32m[09/23 07:43:47 d2.utils.events]: \u001b[0m eta: 0:01:35  iter: 739  total_loss: 0.6485  loss_cls: 0.09793  loss_box_reg: 0.2633  loss_mask: 0.2799  loss_rpn_cls: 0.008818  loss_rpn_loc: 0.01637    time: 0.3704  last_time: 0.4122  data_time: 0.0092  last_data_time: 0.0062   lr: 0.00018482  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":539.851856257,"data":"\u001b[32m[09/23 07:43:55 d2.utils.events]: \u001b[0m eta: 0:01:27  iter: 759  total_loss: 0.7775  loss_cls: 0.1542  loss_box_reg: 0.2954  loss_mask: 0.2675  loss_rpn_cls: 0.006361  loss_rpn_loc: 0.03081    time: 0.3703  last_time: 0.3897  data_time: 0.0079  last_data_time: 0.0067   lr: 0.00018981  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":547.255724779,"data":"\u001b[32m[09/23 07:44:02 d2.utils.events]: \u001b[0m eta: 0:01:20  iter: 779  total_loss: 0.5322  loss_cls: 0.07315  loss_box_reg: 0.2183  loss_mask: 0.2537  loss_rpn_cls: 0.002045  loss_rpn_loc: 0.01187    time: 0.3703  last_time: 0.3445  data_time: 0.0081  last_data_time: 0.0062   lr: 0.00019481  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":554.533396611,"data":"\u001b[32m[09/23 07:44:10 d2.utils.events]: \u001b[0m eta: 0:01:13  iter: 799  total_loss: 0.583  loss_cls: 0.1178  loss_box_reg: 0.2244  loss_mask: 0.2243  loss_rpn_cls: 0.002606  loss_rpn_loc: 0.01259    time: 0.3701  last_time: 0.3479  data_time: 0.0088  last_data_time: 0.0061   lr: 0.0001998  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":562.09889552,"data":"\u001b[32m[09/23 07:44:17 d2.utils.events]: \u001b[0m eta: 0:01:05  iter: 819  total_loss: 0.6812  loss_cls: 0.1063  loss_box_reg: 0.2592  loss_mask: 0.2674  loss_rpn_cls: 0.005045  loss_rpn_loc: 0.01705    time: 0.3703  last_time: 0.3368  data_time: 0.0104  last_data_time: 0.0068   lr: 0.0002048  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":569.577541326,"data":"\u001b[32m[09/23 07:44:25 d2.utils.events]: \u001b[0m eta: 0:00:58  iter: 839  total_loss: 0.6475  loss_cls: 0.1354  loss_box_reg: 0.2331  loss_mask: 0.2683  loss_rpn_cls: 0.003228  loss_rpn_loc: 0.01554    time: 0.3703  last_time: 0.3450  data_time: 0.0073  last_data_time: 0.0085   lr: 0.00020979  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":577.105613046,"data":"\u001b[32m[09/23 07:44:32 d2.utils.events]: \u001b[0m eta: 0:00:51  iter: 859  total_loss: 0.5261  loss_cls: 0.08092  loss_box_reg: 0.2062  loss_mask: 0.2242  loss_rpn_cls: 0.001237  loss_rpn_loc: 0.01276    time: 0.3705  last_time: 0.4097  data_time: 0.0084  last_data_time: 0.0081   lr: 0.00021479  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":584.639586156,"data":"\u001b[32m[09/23 07:44:40 d2.utils.events]: \u001b[0m eta: 0:00:43  iter: 879  total_loss: 0.5345  loss_cls: 0.07986  loss_box_reg: 0.2093  loss_mask: 0.1887  loss_rpn_cls: 0.0009999  loss_rpn_loc: 0.008154    time: 0.3706  last_time: 0.3908  data_time: 0.0076  last_data_time: 0.0079   lr: 0.00021978  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":592.133724965,"data":"\u001b[32m[09/23 07:44:47 d2.utils.events]: \u001b[0m eta: 0:00:36  iter: 899  total_loss: 0.6179  loss_cls: 0.1105  loss_box_reg: 0.2395  loss_mask: 0.2561  loss_rpn_cls: 0.008318  loss_rpn_loc: 0.01785    time: 0.3707  last_time: 0.3466  data_time: 0.0090  last_data_time: 0.0072   lr: 0.00022478  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":599.759886771,"data":"\u001b[32m[09/23 07:44:55 d2.utils.events]: \u001b[0m eta: 0:00:29  iter: 919  total_loss: 0.4821  loss_cls: 0.07783  loss_box_reg: 0.1655  loss_mask: 0.2053  loss_rpn_cls: 0.0013  loss_rpn_loc: 0.01239    time: 0.3709  last_time: 0.4113  data_time: 0.0079  last_data_time: 0.0068   lr: 0.00022977  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":607.564687644,"data":"\u001b[32m[09/23 07:45:03 d2.utils.events]: \u001b[0m eta: 0:00:22  iter: 939  total_loss: 0.5789  loss_cls: 0.1175  loss_box_reg: 0.2348  loss_mask: 0.2183  loss_rpn_cls: 0.002596  loss_rpn_loc: 0.01899    time: 0.3713  last_time: 0.3916  data_time: 0.0087  last_data_time: 0.0098   lr: 0.00023477  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":614.932708254,"data":"\u001b[32m[09/23 07:45:10 d2.utils.events]: \u001b[0m eta: 0:00:14  iter: 959  total_loss: 0.5084  loss_cls: 0.08297  loss_box_reg: 0.1746  loss_mask: 0.235  loss_rpn_cls: 0.002214  loss_rpn_loc: 0.01299    time: 0.3713  last_time: 0.3617  data_time: 0.0084  last_data_time: 0.0086   lr: 0.00023976  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":622.746722669,"data":"\u001b[32m[09/23 07:45:18 d2.utils.events]: \u001b[0m eta: 0:00:07  iter: 979  total_loss: 0.5914  loss_cls: 0.08434  loss_box_reg: 0.235  loss_mask: 0.2241  loss_rpn_cls: 0.002513  loss_rpn_loc: 0.01149    time: 0.3716  last_time: 0.3726  data_time: 0.0085  last_data_time: 0.0177   lr: 0.00024476  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":630.929186452,"data":"\u001b[32m[09/23 07:45:26 d2.utils.events]: \u001b[0m eta: 0:00:00  iter: 999  total_loss: 0.6494  loss_cls: 0.1022  loss_box_reg: 0.2411  loss_mask: 0.252  loss_rpn_cls: 0.002364  loss_rpn_loc: 0.0139    time: 0.3716  last_time: 0.3077  data_time: 0.0075  last_data_time: 0.0069   lr: 0.00024975  max_mem: 1777M\n"}
,{"stream_name":"stdout","time":630.930411314,"data":"\u001b[32m[09/23 07:45:26 d2.engine.hooks]: \u001b[0mOverall training speed: 998 iterations in 0:06:10 (0.3716 s / it)\n"}
,{"stream_name":"stdout","time":630.932104437,"data":"\u001b[32m[09/23 07:45:26 d2.engine.hooks]: \u001b[0mTotal training time: 0:06:25 (0:00:14 on hooks)\n"}
,{"stream_name":"stdout","time":631.852853369,"data":"\u001b[32m[09/23 07:45:27 d2.checkpoint.detection_checkpoint]: \u001b[0m[DetectionCheckpointer] Loading from /kaggle/working/outputs/model_final.pth ...\n"}
,{"stream_name":"stdout","time":633.854559424,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:45:29 d2.data.datasets.coco]: \u001b[0m\n"}
,{"stream_name":"stdout","time":633.854596514,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"}
,{"stream_name":"stdout","time":633.854603187,"data":"\n"}
,{"stream_name":"stdout","time":633.855964849,"data":"\u001b[32m[09/23 07:45:29 d2.data.datasets.coco]: \u001b[0mLoaded 18 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/valid/valid.json\n"}
,{"stream_name":"stdout","time":633.862563388,"data":"\u001b[32m[09/23 07:45:29 d2.data.build]: \u001b[0mDistribution of instances among all 4 categories:\n"}
,{"stream_name":"stdout","time":633.862601189,"data":"\u001b[36m|   category    | #instances   | category   | #instances   | category   | #instances   |\n"}
,{"stream_name":"stdout","time":633.862608844,"data":"|:-------------:|:-------------|:-----------|:-------------|:-----------|:-------------|\n"}
,{"stream_name":"stdout","time":633.862614688,"data":"| leaf, flowe.. | 0            | 0          | 27           | 1          | 19           |\n"}
,{"stream_name":"stdout","time":633.862620603,"data":"|       2       | 36           |            |              |            |              |\n"}
,{"stream_name":"stdout","time":633.862626214,"data":"|     total     | 82           |            |              |            |              |\u001b[0m\n"}
,{"stream_name":"stdout","time":633.864171798,"data":"\u001b[32m[09/23 07:45:29 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n"}
,{"stream_name":"stdout","time":633.865624594,"data":"\u001b[32m[09/23 07:45:29 d2.data.common]: \u001b[0mSerializing the dataset using: \u003cclass 'detectron2.data.common._TorchSerializedList'\u003e\n"}
,{"stream_name":"stdout","time":633.867186249,"data":"\u001b[32m[09/23 07:45:29 d2.data.common]: \u001b[0mSerializing 18 elements to byte tensors and concatenating them all ...\n"}
,{"stream_name":"stdout","time":633.868659769,"data":"\u001b[32m[09/23 07:45:29 d2.data.common]: \u001b[0mSerialized dataset takes 0.02 MiB\n"}
,{"stream_name":"stdout","time":633.870068823,"data":"\u001b[32m[09/23 07:45:29 d2.evaluation.evaluator]: \u001b[0mStart inference on 18 batches\n"}
,{"stream_name":"stdout","time":635.094665576,"data":"\u001b[32m[09/23 07:45:30 d2.evaluation.evaluator]: \u001b[0mInference done 11/18. Dataloading: 0.0016 s/iter. Inference: 0.0821 s/iter. Eval: 0.0064 s/iter. Total: 0.0901 s/iter. ETA=0:00:00\n"}
,{"stream_name":"stdout","time":635.766430367,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.evaluator]: \u001b[0mTotal inference time: 0:00:01.213719 (0.093363 s / iter per device, on 1 devices)\n"}
,{"stream_name":"stdout","time":635.769214269,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.evaluator]: \u001b[0mTotal inference pure compute time: 0:00:01 (0.080777 s / iter per device, on 1 devices)\n"}
,{"stream_name":"stdout","time":635.771225527,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mPreparing results for COCO format ...\n"}
,{"stream_name":"stdout","time":635.773198027,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mSaving results to ./outputs/coco_instances_results.json\n"}
,{"stream_name":"stdout","time":635.778019995,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mEvaluating predictions with unofficial COCO API...\n"}
,{"stream_name":"stdout","time":635.779417428,"data":"Loading and preparing results...\n"}
,{"stream_name":"stdout","time":635.779434434,"data":"DONE (t=0.00s)\n"}
,{"stream_name":"stdout","time":635.779440883,"data":"creating index...\n"}
,{"stream_name":"stdout","time":635.77944617,"data":"index created!\n"}
,{"stream_name":"stdout","time":635.779451855,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mEvaluate annotation type *bbox*\n"}
,{"stream_name":"stdout","time":635.789318341,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.evaluate() finished in 0.01 seconds.\n"}
,{"stream_name":"stdout","time":635.797159053,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mAccumulating evaluation results...\n"}
,{"stream_name":"stdout","time":635.807632373,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.accumulate() finished in 0.02 seconds.\n"}
,{"stream_name":"stdout","time":635.810643959,"data":" Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.302\n"}
,{"stream_name":"stdout","time":635.810661179,"data":" Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.474\n"}
,{"stream_name":"stdout","time":635.810671544,"data":" Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.357\n"}
,{"stream_name":"stdout","time":635.810677171,"data":" Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n"}
,{"stream_name":"stdout","time":635.810682026,"data":" Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.304\n"}
,{"stream_name":"stdout","time":635.810686646,"data":" Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.263\n"}
,{"stream_name":"stdout","time":635.810691178,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.138\n"}
,{"stream_name":"stdout","time":635.810695681,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.391\n"}
,{"stream_name":"stdout","time":635.810700604,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.392\n"}
,{"stream_name":"stdout","time":635.810705254,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n"}
,{"stream_name":"stdout","time":635.810710319,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371\n"}
,{"stream_name":"stdout","time":635.810714862,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.364\n"}
,{"stream_name":"stdout","time":635.810719904,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for bbox: \n"}
,{"stream_name":"stdout","time":635.810725438,"data":"|   AP   |  AP50  |  AP75  |  APs  |  APm   |  APl   |\n"}
,{"stream_name":"stdout","time":635.81073265,"data":"|:------:|:------:|:------:|:-----:|:------:|:------:|\n"}
,{"stream_name":"stdout","time":635.81074006,"data":"| 30.219 | 47.387 | 35.729 |  nan  | 30.435 | 26.317 |\n"}
,{"stream_name":"stdout","time":635.812444438,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mSome metrics cannot be computed and is shown as NaN.\n"}
,{"stream_name":"stdout","time":635.814914009,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mPer-category bbox AP: \n"}
,{"stream_name":"stdout","time":635.814931885,"data":"| category               | AP     | category   | AP     | category   | AP     |\n"}
,{"stream_name":"stdout","time":635.814941054,"data":"|:-----------------------|:-------|:-----------|:-------|:-----------|:-------|\n"}
,{"stream_name":"stdout","time":635.814950236,"data":"| leaf, flower and fruit | nan    | 0          | 11.429 | 1          | 39.127 |\n"}
,{"stream_name":"stdout","time":635.814955966,"data":"| 2                      | 40.100 |            |        |            |        |\n"}
,{"stream_name":"stdout","time":635.831025164,"data":"Loading and preparing results...\n"}
,{"stream_name":"stdout","time":635.831060554,"data":"DONE (t=0.00s)\n"}
,{"stream_name":"stdout","time":635.831066509,"data":"creating index...\n"}
,{"stream_name":"stdout","time":635.831071302,"data":"index created!\n"}
,{"stream_name":"stdout","time":635.831076148,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mEvaluate annotation type *segm*\n"}
,{"stream_name":"stdout","time":635.832967364,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.evaluate() finished in 0.01 seconds.\n"}
,{"stream_name":"stdout","time":635.834387149,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mAccumulating evaluation results...\n"}
,{"stream_name":"stdout","time":635.849730149,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.accumulate() finished in 0.02 seconds.\n"}
,{"stream_name":"stdout","time":635.852968257,"data":" Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.268\n"}
,{"stream_name":"stdout","time":635.853013325,"data":" Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.482\n"}
,{"stream_name":"stdout","time":635.85302032,"data":" Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.266\n"}
,{"stream_name":"stdout","time":635.853026725,"data":" Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n"}
,{"stream_name":"stdout","time":635.853032566,"data":" Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.290\n"}
,{"stream_name":"stdout","time":635.853037529,"data":" Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.233\n"}
,{"stream_name":"stdout","time":635.853043059,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.134\n"}
,{"stream_name":"stdout","time":635.853052344,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.349\n"}
,{"stream_name":"stdout","time":635.853058969,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.350\n"}
,{"stream_name":"stdout","time":635.853080544,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n"}
,{"stream_name":"stdout","time":635.853086828,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.347\n"}
,{"stream_name":"stdout","time":635.853104345,"data":" Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.324\n"}
,{"stream_name":"stdout","time":635.853111104,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for segm: \n"}
,{"stream_name":"stdout","time":635.853117005,"data":"|   AP   |  AP50  |  AP75  |  APs  |  APm   |  APl   |\n"}
,{"stream_name":"stdout","time":635.853122229,"data":"|:------:|:------:|:------:|:-----:|:------:|:------:|\n"}
,{"stream_name":"stdout","time":635.853127204,"data":"| 26.769 | 48.197 | 26.573 |  nan  | 29.003 | 23.334 |\n"}
,{"stream_name":"stdout","time":635.854207133,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mSome metrics cannot be computed and is shown as NaN.\n"}
,{"stream_name":"stdout","time":635.85871157,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mPer-category segm AP: \n"}
,{"stream_name":"stdout","time":635.858754408,"data":"| category               | AP     | category   | AP     | category   | AP     |\n"}
,{"stream_name":"stdout","time":635.858761686,"data":"|:-----------------------|:-------|:-----------|:-------|:-----------|:-------|\n"}
,{"stream_name":"stdout","time":635.858767208,"data":"| leaf, flower and fruit | nan    | 0          | 11.675 | 1          | 31.041 |\n"}
,{"stream_name":"stdout","time":635.858772523,"data":"| 2                      | 37.590 |            |        |            |        |\n"}
,{"stream_name":"stdout","time":635.860120683,"data":"OrderedDict([('bbox', {'AP': 30.218943392039694, 'AP50': 47.386900130198846, 'AP75': 35.72921800473737, 'APs': nan, 'APm': 30.43451625382318, 'APl': 26.31694060377811, 'AP-leaf, flower and fruit': nan, 'AP-0': 11.42892373885882, 'AP-1': 39.12745560270313, 'AP-2': 40.10045083455714}), ('segm', {'AP': 26.768528027464377, 'AP50': 48.19729533929002, 'AP75': 26.573458292849743, 'APs': nan, 'APm': 29.0032162556915, 'APl': 23.334085433968955, 'AP-leaf, flower and fruit': nan, 'AP-0': 11.674782020116336, 'AP-1': 31.04055405540554, 'AP-2': 37.59024800687124})])\n"}
,{"stream_name":"stderr","time":641.251753977,"data":"/opt/conda/lib/python3.10/site-packages/traitlets/traitlets.py:2930: FutureWarning: --Exporter.preprocessors=[\"remove_papermill_header.RemovePapermillHeader\"] for containers is deprecated in traitlets 5.0. You can pass `--Exporter.preprocessors item` ... multiple times to add items to a list.\n"}
,{"stream_name":"stderr","time":641.252033612,"data":"  warn(\n"}
,{"stream_name":"stderr","time":641.271251502,"data":"[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n"}
,{"stream_name":"stderr","time":641.299639329,"data":"[NbConvertApp] Converting notebook __notebook__.ipynb to notebook\n"}
,{"stream_name":"stderr","time":641.812192464,"data":"[NbConvertApp] Writing 1563115 bytes to __notebook__.ipynb\n"}
,{"stream_name":"stderr","time":643.508675438,"data":"/opt/conda/lib/python3.10/site-packages/traitlets/traitlets.py:2930: FutureWarning: --Exporter.preprocessors=[\"nbconvert.preprocessors.ExtractOutputPreprocessor\"] for containers is deprecated in traitlets 5.0. You can pass `--Exporter.preprocessors item` ... multiple times to add items to a list.\n"}
,{"stream_name":"stderr","time":643.508732164,"data":"  warn(\n"}
,{"stream_name":"stderr","time":643.511626803,"data":"[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n"}
,{"stream_name":"stderr","time":643.556443524,"data":"[NbConvertApp] Converting notebook __notebook__.ipynb to html\n"}
,{"stream_name":"stderr","time":644.538437008,"data":"[NbConvertApp] Support files will be in __results___files/\n"}
,{"stream_name":"stderr","time":644.538518612,"data":"[NbConvertApp] Making directory __results___files\n"}
,{"stream_name":"stderr","time":644.539060619,"data":"[NbConvertApp] Making directory __results___files\n"}
,{"stream_name":"stderr","time":644.539504448,"data":"[NbConvertApp] Making directory __results___files\n"}
,{"stream_name":"stderr","time":644.540311438,"data":"[NbConvertApp] Writing 357130 bytes to __results__.html\n"}
]